{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "jawis = glob.glob('words/*.png')\n",
    "rumis = glob.glob('new/*.png') + glob.glob('hand/words/*.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import map_coordinates\n",
    "from scipy.ndimage.filters import gaussian_filter\n",
    "\n",
    "def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None):\n",
    "    if random_state is None:\n",
    "        random_state = np.random.RandomState(None)\n",
    "\n",
    "    shape = image.shape\n",
    "    shape_size = shape[:2]\n",
    "    \n",
    "    blur_size = int(4*sigma) | 1\n",
    "    dx = alpha * cv2.GaussianBlur((random_state.rand(*shape) * 2 - 1),\n",
    "                                  ksize=(blur_size, blur_size),\n",
    "                                  sigmaX=sigma)\n",
    "    dy = alpha * cv2.GaussianBlur((random_state.rand(*shape) * 2 - 1),\n",
    "                                  ksize=(blur_size, blur_size),\n",
    "                                  sigmaX=sigma)\n",
    "\n",
    "    x, y = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))\n",
    "    indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1))\n",
    "\n",
    "    image =  map_coordinates(image, indices, order=1, mode='constant').reshape(shape)\n",
    "    \n",
    "    # Random affine\n",
    "    center_square = np.float32(shape_size) // 2\n",
    "    square_size = min(shape_size) // 4\n",
    "    pts1 = np.float32([center_square + square_size,\n",
    "                       [center_square[0]+square_size, \n",
    "                        center_square[1]-square_size],\n",
    "                       center_square - square_size])\n",
    "    pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)\n",
    "    M = cv2.getAffineTransform(pts1, pts2)\n",
    "    image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_CONSTANT)\n",
    "\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask import delayed\n",
    "import dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "def process_jawi(i):\n",
    "    jawi = jawis[i]\n",
    "    img = cv2.imread(jawi)\n",
    "    im = cv2.bitwise_not(img)\n",
    "    im = im[:, np.min(np.where(im > 0)[1]) - 50:]\n",
    "    im = im[:,:,0]\n",
    "    im = elastic_transform(im, im.shape[1] * 5, im.shape[1] * 0.2, im.shape[1] * 0.001)\n",
    "    return im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_jawi = []\n",
    "for i in range(len(jawis)):\n",
    "    im = delayed(process_jawi)(i)\n",
    "    train_jawi.append(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 40min 50s, sys: 13.4 s, total: 41min 3s\n",
      "Wall time: 1min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "train_jawi = dask.compute(*train_jawi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31051"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_jawi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4f4b869a58>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = train_jawi[0]\n",
    "plt.imshow(im, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34266"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rumis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4f4b78a8d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = cv2.imread(rumis[0])\n",
    "im = im[:,: np.max(np.where(im > 0)[1]) + 50]\n",
    "im = im[:,:,0]\n",
    "im = elastic_transform(im, im.shape[1] * 5, im.shape[1] * 0.2, im.shape[1] * 0.001)\n",
    "plt.imshow(im, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_rumi(i):\n",
    "    rumi = rumis[i]\n",
    "    im = cv2.imread(rumi)\n",
    "    im = im[:,: np.max(np.where(im > 0)[1]) + 50]\n",
    "    im = im[:,:,0]\n",
    "    im = elastic_transform(im, im.shape[1] * 5, im.shape[1] * 0.2, im.shape[1] * 0.001)\n",
    "    return im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_rumi = []\n",
    "for i in range(len(rumis)):\n",
    "    im = delayed(process_rumi)(i)\n",
    "    train_rumi.append(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 43min 7s, sys: 15.7 s, total: 43min 23s\n",
      "Wall time: 1min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "train_rumi = dask.compute(*train_rumi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34266"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_rumi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4f4b675780>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_rumi[0], cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('detect-dataset.pkl', 'wb') as fopen:\n",
    "    pickle.dump({'positive': train_jawi, 'negative': train_rumi}, fopen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
